Create README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: robotics
+library_name: transformers
+tags:
+- multimodal
+- robotics
+- vision-language-action
+- univla
+- vla-arena
+- imitation-learning
+datasets:
+- VLA-Arena/VLA_Arena_L0_L_rlds
+---
+
+# UniVLA (VLA-Arena Fine-tuned)
+
+## About VLA-Arena
+
+**VLA-Arena** is a comprehensive benchmark designed to quantitatively understand the limits and failure modes of Vision-Language-Action (VLA) models. While VLAs are advancing towards generalist robot policies, measuring their true capability frontiers remains challenging. VLA-Arena addresses this by proposing a novel structured task design framework that quantifies difficulty across three orthogonal axes:
+
+1.  **Task Structure**: 170+ tasks grouped into four key dimensions:
+    * **Safety**: Operating reliably under strict constraints.
+    * **Distractor**: Handling environmental unpredictability.
+    * **Extrapolation**: Generalizing to unseen scenarios.
+    * **Long Horizon**: Executing complex, multi-step tasks.
+2.  **Language Command**: Variations in instruction complexity.
+3.  **Visual Observation**: Perturbations in visual input.
+
+Tasks are designed with hierarchical difficulty levels (L0-L2). In this benchmark setting, fine-tuning is typically performed on **L0** tasks to assess the model's ability to generalize to higher difficulty levels and strictly follow safety constraints.
+
+## Model Overview
+
+The model is **UniVLA**, explicitly fine-tuned on demonstration data generated from **VLA-Arena**. UniVLA distinguishes itself by employing a **Latent Action Model (LAM)** to handle action generation, separating the policy learning into a high-level vision-language planner and a low-level latent action decoder.
+
+Unlike typical parameter-efficient fine-tuning (PEFT) approaches where the backbone is frozen, this checkpoint involves training both the VLA backbone components and the dedicated action model.
+
+---
+
+## Model Architecture
+
+
+
+UniVLA utilizes a hierarchical structure involving a VLA backbone for semantic understanding and a specialized Latent Action Model (LAM) for discrete action token generation.
+
+| Component | Description |
+| :--- | :--- |
+| **Backbone** | **VLA** (Vision-Language Backbone) |
+| **Action Generation** | **Latent Action Model (LAM)** |
+| **Action Space** | Discrete Codebook (Size 16) |
+| **Training State** | **Unfrozen** (Both VLA Backbone and Action Model are trained) |
+
+### Key Feature: Latent Action Model (LAM)
+The LAM acts as a specialized tokenizer and predictor for robotic actions. It compresses continuous actions into a compact discrete latent space, allowing for efficient sequence modeling.
+
+| LAM Parameter | Value |
+| :--- | :--- |
+| **Codebook Size** | 16 |
+| **Model Dimension** | 768 |
+| **Latent Dimension** | 128 |
+| **Structure** | 12 Encoder Blocks / 12 Decoder Blocks |
+| **Window Size** | 12 |
+
+---
+
+## Training Details
+
+### Dataset
+This model was trained on the **[VLA-Arena/VLA_Arena_L0_L_rlds](https://huggingface.co/datasets/VLA-Arena/VLA_Arena_L0_L_rlds)** dataset. The data consists of diverse robotic manipulation demonstrations formatted in RLDS (Reinforcement Learning Datasets) standard.
+
+### Hyperparameters
+The training utilized gradient accumulation to achieve an effective batch size of 16. Notably, the backbone was **not frozen**, allowing for deeper adaptation to the VLA-Arena tasks.
+
+| Parameter | Value |
+| :--- | :--- |
+| **Max Training Steps** | 30,000 |
+| **Batch Size (Per Device)** | 8 |
+| **Gradient Accumulation** | 2 steps |
+| **Effective Total Batch Size** | 16 |
+| **Optimizer** | AdamW |
+| **Learning Rate ($\eta$)** | $3.5 \times 10^{-4}$ (Fixed) |
+| **Shuffle Buffer Size** | 16,000 |
+| **Image Augmentation** | Enabled (`TRUE`) |
+
+### LoRA Configuration
+While LoRA was enabled, the training configuration specified that the VLA backbone remained unfrozen, indicating a hybrid or comprehensive fine-tuning approach.
+
+| Parameter | Value |
+| :--- | :--- |
+| **LoRA Rank ($r$)** | 32 |
+| **LoRA Dropout** | 0.0 |
+| **Use 4-bit Quantization** | Disabled (`FALSE`) |
+| **Backbone Freeze** | Disabled (`FALSE`) |
+
+---
+
+## Evaluation & Usage
+
+This model is designed to be evaluated within the **VLA-Arena** benchmark ecosystem. It has been tested across 11 specialized suites with difficulty levels ranging from **L0 (Basic)** to **L2 (Advanced)**.
+
+For detailed evaluation instructions, metrics, and scripts, please refer to the [VLA-Arena repository](https://github.com/PKU-Alignment/VLA-Arena).